Paper Session III-B - Prospects of utilization of the space-purpose temperature sensors for public and commercial use

For the temperature monitoring of units, mechanisms and technological manufacture processes use of sensors which convert temperature to electric signal is preferable. Metal and semiconductor resistance thermometers, thermocouples and thermodiodes are such sensors. Comparative characteristics of these sensors are given in Tab. 1. Temperature ranges which are subject to monitoring and control in a number of the most important branches of engineering are represented by Tab. 2. Comparison of these data shows that in majority of cases temperature has to be measured in the range of 190 ¸ 450 K. It appears that thermodiode sensors are the most suitable for this purpose because they are superior to all other sensors in sensitivity, output signal level, cost and simplicity of use. Their salient feature is the possibility of connection with the measuring unit by means of two-wire connection line of length from some tens meters to some kilometers

Contextual Object Detection with a Few Relevant Neighbors

A natural way to improve the detection of objects is to consider the contextual constraints imposed by the detection of additional objects in a given scene. In this work, we exploit the spatial relations between objects in order to improve detection capacity, as well as analyze various properties of the contextual object detection problem. To precisely calculate context-based probabilities of objects, we developed a model that examines the interactions between objects in an exact probabilistic setting, in contrast to previous methods that typically utilize approximations based on pairwise interactions. Such a scheme is facilitated by the realistic assumption that the existence of an object in any given location is influenced by only few informative locations in space. Based on this assumption, we suggest a method for identifying these relevant locations and integrating them into a mostly exact calculation of probability based on their raw detector responses. This scheme is shown to improve detection results and provides unique insights about the process of contextual inference for object detection. We show that it is generally difficult to learn that a particular object reduces the probability of another, and that in cases when the context and detector strongly disagree this learning becomes virtually impossible for the purposes of improving the results of an object detector. Finally, we demonstrate improved detection results through use of our approach as applied to the PASCAL VOC and COCO datasets

Study of the process e+e−→ppˉe^+e^-\to p\bar{p} in the c.m. energy range from threshold to 2 GeV with the CMD-3 detector

Using a data sample of 6.8 pb$^{-1}$ collected with the CMD-3 detector at the VEPP-2000 $e^+e^-$ collider we select about 2700 events of the $e^+e^- \to p\bar{p}$ process and measure its cross section at 12 energy ponts with about 6\% systematic uncertainty. From the angular distribution of produced nucleons we obtain the ratio $|G_{E}/G_{M}| = 1.49 \pm 0.23 \pm 0.30$

Measurement of the e+e−→K+K−π+π−e^+e^- \to K^+K^-\pi^+\pi^- cross section with the CMD-3 detector at the VEPP-2000 collider

The process $e^+e^- \to K^+K^-\pi^+\pi^-$ has been studied in the center-of-mass energy range from 1500 to 2000\,MeV using a data sample of 23 pb$^{-1}$ collected with the CMD-3 detector at the VEPP-2000 $e^+e^-$ collider. Using about 24000 selected events, the $e^+e^- \to K^+K^-\pi^+\pi^-$ cross section has been measured with a systematic uncertainty decreasing from 11.7\% at 1500-1600\,MeV to 6.1\% above 1800\,MeV. A preliminary study of $K^+K^-\pi^+\pi^-$ production dynamics has been performed

Robustness and Generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work